Coping with Imprecision in Strategic Planning: A Case Study Using Fuzzy SWOT Analysis
Why this work is in the frame
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Bibliographic record
Abstract
In this article, it is shown that using the conventional SWOT analysis in the vicinity of strategic regions in the matrix of internal and external factors, ambiguity can exist in defining final strategies. To cope with this difficulty and to enhance the accuracy of the decision process, a straightforward fuzzy SWOT analysis is presented and exemplified by extracting and analyzing strengths, weaknesses, opportunities and threats in a company known as KPPP. The analysis is performed based on actual field data using 90 external and 85 internal factors and a group of 12 experts. Next to the identification of the fuzzy SWOT matrix, it is shown that the external threats and internal weaknesses of KPPP can have stronger effects compared to its external opportunities and internal strengths.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it